Cellular telecommunications network

ABSTRACT

This disclosure provides a method, and a network node for implementing the method, of switching a base station in a cellular telecommunications network between a first and second mode, in which the base station uses more energy when operating in the first mode than the second mode, wherein the cellular telecommunications network further includes a User Equipment, UE, having a camera, the method including storing visual data including a visual representation of at least a part of the base station; receiving visual data captured by the camera of the UE; performing a computer vision operation, trained on the stored visual data, on the captured visual data to determine whether the visual representation of the base station or part thereof is present in the captured visual data; and, in response initiating a switch in the base station between the first and second modes.

RELATED APPLICATION

The present application claims priority to EP Application No. 19188772.8filed Jul. 29, 2019, and GB Application No.: 1910781.2, filed Jul. 29,2019, which are hereby incorporated herein in their entireties byreference.

TECHNICAL FIELD

The present disclosure relates to a method in a cellulartelecommunications network.

BACKGROUND

Cellular telecommunications networks include a plurality of basestations, each having a coverage area within which the base stationprovides voice and data services to a plurality of User Equipments(UEs). UEs are often mobile and therefore can move from the coveragearea of a current (“serving”) base station to the coverage area ofanother base station. When this occurs, the UE must be transferred tothe other base station (in which the other base station is known as the“target” of that transfer) so that the target base station thereafterprovides voice and data services to the UE.

Base stations of conventional cellular telecommunications networksoperated with transmission powers and frequency bands that permittedcoverage areas of several square kilometers. However, base stations ofmodern cellular telecommunications networks can also utilize frequencybands with relatively high frequencies that correspond to relativelysmall coverage areas. This includes, for example, millimeter wave(mmWave) frequency bands of 30-300 GHz. Furthermore, such highfrequencies have relatively high attenuation through building materials,so that outdoor base stations of modern cellular telecommunicationsnetworks provide relatively poor indoor service. To ensure a goodquality connection with a base station operating in these frequencybands, a UE should have Line of Sight (LoS) with the base station.Furthermore, to maintain connectivity in these modern networks where UEsrequire LoS to the base station, the UE must be transferred between basestations (or between distinct beams of a single base station) morefrequently. This results in a corresponding increase in controlsignaling for the UE to perform radio measurement reporting.

SUMMARY

According to a first aspect of the disclosure, there is provided amethod of switching a base station in a cellular telecommunicationsnetwork between a first and second mode, in which the base station usesmore energy when operating in the first mode than the second mode,wherein the cellular telecommunications network further includes a UserEquipment, UE, having a camera, the method comprising: storing visualdata including a visual representation of at least a part of the basestation; receiving visual data captured by the camera of the UE;performing a computer vision operation, trained on the stored visualdata, on the captured visual data to determine whether the visualrepresentation of the base station or part thereof is present in thecaptured visual data; and, in response initiating a switch in the basestation between the first and second modes.

The determination may be that the visual representation of the basestation is present in the captured visual data, and the switch may befrom the second mode to the first mode. The determination may be thatthe visual representation of the base station is not present in thecaptured visual data, and the switch may be from the first mode to thesecond mode.

The visual representation of at least part of the base station mayfurther include one or more features in the base station's surroundings.

According to a second aspect of the disclosure, there is provided acomputer program product comprising instructions which, when the programis executed by a computer, cause the computer to carry out the method ofthe first aspect of the disclosure. The computer program may be storedon a computer-readable data carrier.

According to a third aspect of the disclosure, there is provided anetwork node in a cellular telecommunications network, the network nodehaving a transceiver, a processor and a memory configured to cooperateto carry out the method of the first aspect of the disclosure. Thenetwork node may be a UE or a base station.

BRIEF DESCRIPTION OF THE FIGURES

In order that the present invention may be better understood,embodiments thereof will now be described, by way of example only, withreference to the accompanying drawings in which:

FIG. 1 is a schematic diagram of an embodiment of a cellulartelecommunications network of the present disclosure.

FIG. 2 is a schematic diagram of a first base station of the network ofFIG. 1.

FIG. 3 is a schematic diagram of a UE of the network of FIG. 1.

FIG. 4 is a schematic diagram of an edge computing node of the networkof FIG. 1.

FIG. 5 is a schematic diagram of a cellular telecommunications networkimplementing a first embodiment of a method of the present disclosure,in a first state.

FIG. 6 is a schematic diagram of the cellular telecommunications networkimplementing the first embodiment of a method of the present disclosure,in a second state.

FIG. 7 is a flow diagram illustrating the first embodiment of the methodof the present disclosure.

FIG. 8 is a schematic diagram of a cellular telecommunications networkimplementing a second embodiment of a method of the present disclosure,in a first state.

FIG. 9 is a schematic diagram of the cellular telecommunications networkimplementing the second embodiment of a method of the presentdisclosure, in a second state.

FIG. 10 is a flow diagram illustrating the second embodiment of themethod of the present disclosure.

FIG. 11 is a schematic diagram of a cellular telecommunications networkimplementing a third embodiment of a method of the present disclosure,in a first state.

FIG. 12 is a schematic diagram of the cellular telecommunicationsnetwork implementing the third embodiment of a method of the presentdisclosure, in a second state.

FIG. 13 is a flow diagram illustrating the third embodiment of themethod of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

A first embodiment of a cellular telecommunications network 1 will nowbe described with reference to FIGS. 1 to 4. The cellulartelecommunications network 1 includes a User Equipment (UE) 10, a firstbase station 20, a second base station 30 and a Mobile Edge Computing(MEC) server 40. FIG. 1 illustrates a first beam of the first basestation 20 being transmitted about a coverage area. Although the firstbase station 20 is likely to transmit a plurality of beams, only thisfirst beam is shown for simplicity. The UE 10 is shown as beingpositioned within the first beam of the first base station 20. FIG. 1also illustrates a first beam of the second base station beingtransmitted about a coverage area. Again, the second base station 30 islikely to transmit a plurality of beams, but only this first beam isshown for simplicity.

The first base station 20 is shown in more detail in FIG. 2. The firstbase station 20 includes a first communications interface 21, aprocessor 23, memory 25 and a second interface 27, all connected via bus29. In this embodiment, the first communications interface 21 is anantenna configured for wireless communications using signals havingfrequencies ranging from 30 GHz to 300 GHz (such signals are known asmillimeter waves, mmWave), and the second communications interface 27 isa wired connection (e.g. optical fiber) to one or more cellular corenetworking nodes (including the MEC 40). The processor 23 and memory 25are configured for facilitating these communications, such as byprocessing and storing data packets sent/received via the first andsecond communications interfaces.

In this embodiment, the second base station 30 is substantially the sameas the first base station 20.

The UE 10 is shown in more detail in FIG. 3. In this embodiment, the UE10 is a virtual reality headset configured for cellulartelecommunications. Accordingly, the UE 10 includes a communicationsinterface 11, a processor 12, memory 13, an optical camera 14, and adisplay 15, all connected via bus 16. In this embodiment, thecommunications interface 11 is an antenna configured for wirelesscommunications using signals having frequencies ranging from 30 GHz to300 GHz. The optical camera 14 is configured for capturing images orvideo (i.e. a sequence of images) in the visible spectrum (that is, ofelectromagnetic radiation having wavelengths in the range of around 400to 700 nanometers).

The MEC 40 is shown in more detail in FIG. 4. In this embodiment, theMEC 40 includes a communications interface 41, a processor 43, andmemory 45, all connected via bus 47. Memory 45 includes a database ofvisual training data for a computer vision learning agent. In thisembodiment, memory 45 includes a database having a first database tableincluding:

-   -   1. a base station identifier uniquely identifying the base        station from any other base station in the network (e.g. an        enhanced Cell Global Identifier, eCGI, for the base station),    -   2. location data for the base station (e.g. the base station's        Global Navigation Satellite System, GNSS, coordinates), and    -   3. a base station image identifier (uniquely identifying images        of that base station in situ) used to look up corresponding        images of that base station in a second database table.

The second database table therefore includes the base station imageidentifier and one or more images of that base station in its real-worldposition (e.g. at a variety of angles). This data is used to train acomputer vision process implemented by processor 43.

Memory 45 is updated with new information for each base station, andinformation on each new base station in the cellular telecommunicationsnetwork. For example, memory 45 may be updated with new images of thefirst and second base stations in their real-world positions on aperiodic basis, and updated with information on a new base station beingadded to the network and one or more images of that base station in itsreal-world position.

The processor 43 of MEC 40 implements a computer vision process by alearning agent 43 a and an inference agent 43 b. The learning agent 43 ais configured to train a machine learning algorithm, in this case aclassification model, based on the visual training data in the database.The classification model maps between each input image from the seconddatabase table and the corresponding base station identifier. Thetrained classification model may then be used by the inference agent 43b.

The learning agent 43 a performs periodic learning operations to updatethe classification algorithm, thus adapting to any new images ofexisting base stations or of images of new base stations.

The inference agent 43 b uses the trained classification model in orderto map between an input image (e.g. an image captured by the opticalcamera 14 of the UE 10) and a base station identifier. This will beexplained in more detail, below.

A first embodiment of a method of the present disclosure will now bedescribed with reference to FIGS. 5 to 7. In this first embodiment, asshown in FIG. 5, the UE 10 is being served by the first base station 20and is positioned within the coverage area of the first base station'sfirst beam. In S1, the UE 10 captures an image via its optical camera14. In this example, the captured image includes the second base station30. The image is transmitted to the MEC 40, via the first base station20.

In S3, the inference agent 43 b takes the captured image as its inputand, using its trained classification model, outputs a base stationidentifier. In this example, the inference agent 43 b uses its trainedclassification model to output a base station image identifier (based ona mapping between the captured image and one or more images of thesecond base station 30 stored in the second database table). Theprocessor 43 then uses the stored mapping (from the first databasetable) to map between the base station image identifier and the basestation identifier (e.g. eCGI) for the second base station 30.

In S5, the MEC 40 sends a message to the first base station 20including 1) the base station identifier (e.g. eCGI) of the second basestation 30, and 2) an indicator that the UE 10 has LoS to the secondbase station 30.

In S6, the first base station 20 consults its Neighbor Relations Table(NRT) to determine whether or not the second base station 30 is a knownneighbor. If not, then the first base station 20 establishes an X2connection (that is, an inter-base station connection) with the secondbase station 30 and records information for the second base station 30in its NRT.

In S7, the first base station 20 sends an X2 message to the second basestation 30 identifying the UE 10 and the UE's GNSS coordinates. In S9,the second base station 30 reacts to this message by reconfiguring itsfirst beam so that its coverage area covers the UE 10. That is, thesecond base station 30 may calculate a distance and an orientation angleto the UE 10 based on its own GNSS coordinates and the UE's GNSScoordinates. The second base station 30 may then reconfigure its firstbeam to transmit at the calculated orientation angle and over thecalculated distance. In S11, the first base station 20 receivesconfirmation that the UE 10 is now within the coverage area of the firstbeam of the second base station 30. In this embodiment, thisconfirmation is via a confirmation message from the second base station30. In S13, the first base station 20 initiates a transfer of the UE 10to the second base station 30 so that the UE 10 is thereafter served bythe second base station 30. Following this reconfiguration, the cellulartelecommunications network 1 is as shown in FIG. 6.

In cellular telecommunications networks utilizing relatively highfrequency bands (such as the mmWave frequency band used in thisembodiment), UEs have a better quality connection when the UE andserving base station have LoS. Accordingly, this embodiment utilizes theoptical camera of the UE 10 and a computer vision process to determinethat the UE 10 has LoS with the base station and, in response, initiatesa transfer of the UE to that base station. This embodiment thereforeomits the typical UE measurement reporting parts of a traditionalhandover. Such steps are unnecessary following this positivedetermination that the UE 10 has LoS with the second base station 30.Furthermore, this embodiment supports a transfer of the UE 10 to anotherbase station when such a transfer would not be possible with atraditional handover. That is, the second base station's first beam doesnot initially cover the UE 10 (as shown in FIG. 5), so the UE'smeasurement reports would not identify the second base station 30 (suchthat the first base station's NRT would not identify the second basestation 30) and the transfer would not be possible. However, asillustrated above, by identifying that the UE 10 has LoS with the secondbase station 30 from the captured image, the second base station 30 mayreconfigure in order to provide service in a coverage area that coversthe UE 10 so that a transfer to the second base station 30 becomespossible.

In the above embodiment, the first base station 20 reacts to the messagefrom the MEC server 40 by performing several steps resulting in atransfer of the UE 10 to the second base station 30. This may be due to,for example, the connection between the UE and first base stationdegrading (e.g. if measurement reports or the visual data indicate thatthe distance between the UE and first base station are increasing).However, the skilled person will understand that this reaction may alsobe used to balance network load.

A second embodiment of a method of the present disclosure will now bedescribed with reference to FIGS. 8 to 10. This second embodimentutilizes the same cellular telecommunications network of the firstembodiment and therefore the same reference numerals will be used.Furthermore, S1 to S6 are also performed in this second embodiment, sothat the MEC 40 sends a message to the first base station 20including 1) the base station identifier (e.g. eCGI) of the second basestation 30, and 2) and indicator that the UE 10 has LoS to the secondbase station 30, and, in response, the first base station 20confirms/establishes an X2 connection with the second base station 30.

The first and second base stations 20, 30 may operate in either a first(active) state or a second (energy saving) state. The first and secondbase stations 20, 30 use more energy when in the active state than whenin the energy saving state. Following S6, the first and second basestations 20, 30 communicate over their X2 connection to update theirrespective NRTs with information on the neighboring base station. Thisinformation includes the operating state of the base station. In thissecond embodiment, as shown in FIG. 8, the second base station 30 isinitially in an energy saving state. In S8, the first base station 20sends an activation signal (over the X2 connection) to the second basestation 30. This activation signal causes the second base station 30 toswitch from the energy saving state to the active (i.e. non-energysaving state) mode of operation. In this second embodiment, theactivation signal further includes an identifier for the UE 10 and theUE's GNSS coordinates. Similar to the first embodiment, the second basestation 30 responds to this information by reconfiguring its first beamso that its coverage area covers the UE 10. Following thisreconfiguration, the second base station 30 sends a message to the firstbase station 20 confirming that the UE 10 is now within the coveragearea of the first beam of the second base station 30, and, in response,the first base station 20 initiates a transfer of the UE 10 to thesecond base station 30 (that is, implementing S9, S11 and S13 of thefirst embodiment). Following these, the cellular telecommunicationsnetwork is in the configuration shown in FIG. 9.

This second embodiment therefore provides a further benefit in detectingLoS between a UE and a base station based on an image captured from theUE's optical camera in that, in response, the base station may beswitched from an energy saving mode of operation to a normal (active)mode of operation. The base station may then be used as a handovertarget. This is also possible when the second base station 30 is notalready known to the first base station 20 (that is, the second basestation 30 is not a member of the first base station's NRT), as theidentification of the second base station 30 from the image captured bythe UE 10 allows the first base station 20 to identify the second basestation 30 as a neighbor even though the UE is not present in the secondbase station's first beam.

In an enhancement to this second embodiment, the MEC server 40 continuesto process visual data received from the UE 10 and determines that theUE 10 subsequently loses LoS with the first base station 20. Followingthis determination, the MEC 40 sends an instruction message to the firstbase station 20 to switch from its normal (active) mode of operation toan energy saving mode of operation. This second embodiment thereforeuses LoS information to switch base stations into and out of energysaving mode.

A third embodiment of a method of the present disclosure will now bedescribed with reference to FIGS. 11 to 13. This third embodiment alsoutilizes the same cellular telecommunications network of the firstembodiment and therefore the same reference numerals will be used. InS17 the UE 10 captures a sequence of images using its optical camera.This sequence of images is sent to the MEC server 40 via the first basestation 20.

In S19, the MEC server 40 processes the sequence of images anddetermines that both the first and second base station 20, 30 arepresent (using the inference agent 43 b and the trained classificationmodel, as discussed in the first embodiment above).

In S21, the MEC server 40 is also able to identify a moving object inthe sequence of images. This is achieved by background subtraction todetermine that the object has a different position in different imagesof the sequence of images. In this example, the MEC server 40 implementsthe background subtraction method detailed in “ViBe: A UniversalBackground Subtraction Algorithm for Video Sequences,” O. Barnich and M.Van Droogenbroeck, IEEE Transactions on Image Processing, vol. 20, no.6, pp. 1709-1724, June 2011.

In S23, the MEC server 40 determines whether the moving object is on apath such that it will block LoS between the UE 10 and first basestation 20. This is based on both an object tracking function (such as“Deep Learning for Moving Object Detection and Tracking from a SingleCamera in Unmanned Aerial Vehicles (UAVs)”, Dong Hye Ye et al., IS&TInternational Symposium on Electronic Imaging 2018) and a relative depthdetermination function (such as “Single-Image Depth Perception in theWild”, Chen et al., 30th Conference on Neural Information ProcessingSystems). In this example, the result of this determination is that themoving object will block LoS between the UE 10 and first base station20. In response to this positive determination, the MEC server 40 sendsa message to the first base station 20 to trigger a transfer of the UE10 to the second base station 30 (25). The network is then in theconfiguration shown in FIG. 12.

The third embodiment therefore provides the advantage that a futureblockage between the UE and serving base station may be predicted and,in response, a pre-emptive transfer of the UE to another base stationwith which it has LoS may be initiated. The UE therefore continues toreceive service from a base station with which it has LoS, thus ensuringcontinuity of Quality of Service (QoS). The skilled person willunderstand that it is non-essential for the blockage to be caused by amoving object. That is, the blockage may be predicted based on any formof relative movement between the UE, object and base station. Forexample, the object may be stationary, but the motion of the basestation and/or UE may result in a loss of LoS between the UE and basestation, which may be predicted from the sequence of images and, inresponse, a pre-emptive transfer may be initiated. Furthermore, theskilled person will understand that the third embodiment may beimplemented by the MEC server 40 determining the probability that therelative motion between the UE, base station and object is such that theobject will block LoS between the UE and base station, and comparingthis probability to a threshold.

In the above embodiments, the MEC server 40 included memory 45 having afirst database table storing a base station identifier for each basestation and base station image identifier(s) for one or more images ofthat base station (the images being stored in a second database table).The images were of that exact base station as installed in thereal-world. Furthermore, there may be a plurality of images of thatexact base station, in which each image is from a different imagecapture position. By using this data to train the classification model,the MEC server 40 can then use the trained classification model touniquely identify the base station that is within the captured visualdata from the UE 10. The skilled person will understand that it isbeneficial to use an image (or images) of the base station including oneor more distinctive features in the base station's surroundings. Thesedistinctive features (and their spatial relationship to the basestation) may improve the accuracy of the classification model.

The skilled person will also understand that, in some scenarios, only apart of the base station may be visible (e.g. the antenna) with theremainder of the base station being located inside a housing andinvisible from the point of view of many UEs. For example, some modernbase stations are located inside lampposts, with the antenna extendingfrom the top of the lamppost and the remainder of the base station beinglocated inside the lamppost housing. Of course, the image(s) used totrain the classification model would then include only the visible partof the base station (the antenna) and the other parts of the image (suchas the lamppost) form part of the distinctive features in itssurroundings that are used to train the classification model torecognize that base station.

It is also non-essential that the images of the base station are of thatexact base station as installed in the real-world. In an alternativearrangement, memory 45 includes a third database table having a basestation model identifier identifying a model of base station (of whichthere may be several base stations in the network of this particularmodel) and one or more images of this model of base station. The firstdatabase table may also further identify the model of base station foreach base station in the network. The MEC server's learning agent 43 ais then configured to train a further machine learning algorithm, againa classification model, based on the images of the third database table.This second classification model maps between each image from the thirddatabase table and the corresponding base station model identifier. Theinference agent 43 b may then use this second classification model (e.g.in the event the classification model of the first embodiment above doesnot successfully identify an exact base station) to identify the modelof base station within the captured image from the UE 10. The inferenceagent 43 b has not yet uniquely identified the second base station 30 atthis stage, as several base stations may be based on that model.Accordingly, the inference agent 43 b uses location data for the UE 10to determine that the UE 10 is within a threshold distance of the secondbase station 30. The inference agent 43 b may combine this data (thatthe UE 10 is within the threshold distance of the second base station 30and that the captured image from the UE 10 includes the model of basestation associated with the second base station 30) to determine that itis the second base station 30 in the captured image. The inference agent43 b then outputs the base station identifier (e.g. the enhanced CellGlobal Identifier).

In the above embodiments, the computer vision operation is performed inthe MEC server 40. However, this is non-essential and the method couldbe performed in any single node or distributed across several nodes inthe network. For example, each base station in the network may store thesame data that is stored in memory 45 of the MEC server 40, but limitedonly to nearby base stations (e.g. only those base stations identifiedin its Neighbor Relations Table, NRT). In this scenario, when a UEconnects to the base station, the base station may forward the data tothe UE so that the computer vision operations may be performed locallyin the UE. Following a positive identification of another base stationwithin an image captured by the UE, the UE may send a message to thebase station indicating that it has LoS with the other base station.Following a transfer to the other base station, the UE may then receivenew data from the other base station for its computer vision operations.

Furthermore, in the above embodiments, the UE 10 and first and secondbase stations 20, 30 are configured for mmWave communications. Thebenefits are particularly relevant for such communications due to therequirement for LoS (or near LoS) between the UE and base station.However, the skilled person will understand that this is non-essential.That is, the UE 10 and first and second base stations 20, 30 maycommunicate using any frequency band and cellular telecommunicationsprotocol and realize these benefits, as confirming LoS will nonethelessindicate that the UE and base station will have a good qualityconnection and would furthermore allow the handover process to skip themeasurement reporting step, thus saving network resources (includinge.g. bandwidth, power and memory) which would have been used on themeasurement reporting.

The MEC server includes memory for storing data on the base stations inthe network. This may be of a subset of base stations in the network(such as those in the geographical region of the MEC server) to reducestorage requirements. In this scenario, the database may be updated withany moving base station that moves into or out of the geographicalregion.

In the above embodiments, the UE 10 is a virtual reality headset.However, this is also non-essential, and the UE may be any form of userequipment that includes a camera for capturing visual data in thevisible light spectrum and a communications interface for communicatingvia a cellular telecommunications protocol. The skilled person will alsounderstand that the present invention is not limited to the use ofvisible spectrum (although that may be preferable due to theavailability of optical cameras on UEs). That is, the computer visionprocesses outlined above may operate in other parts of theelectromagnetic spectrum, such as infrared, and thus the methods of thepresent invention may be implemented based on visual data captured bycameras operating outside the visible spectrum.

In a further enhancement to the above embodiments, a successful transferof the UE to the target base station may be reported back to the MECserver. This success may be based on both the UE connecting to thetarget base station and also the UE receiving connection characteristics(e.g. Signal to Noise Ratio, SNR, or throughput) which is indicative ofLoS. This data may be used to add the image captured by the UE to thesecond database table, which improves the body of training data for thelearning agent, and also acts as a form of supervised learning toindicate that the previous classification model was accurate.

The skilled person will understand that any combination of features ispossible within the scope of the invention, as claimed.

1. A method of switching a base station in a cellular telecommunicationsnetwork between a first and a second mode, in which the base stationuses more energy when operating in the first mode than in the secondmode, wherein the cellular telecommunications network further includes aUser Equipment (UE) having a camera, the method comprising: storingvisual data including a visual representation of at least a part of thebase station; receiving visual data captured by the camera of the UE;performing a computer vision operation, trained on the stored visualdata, on the captured visual data to determine whether the visualrepresentation of the base station or part thereof is present in thecaptured visual data; and, in response initiating a switch in the basestation between the first mode and the second mode.
 2. The method asclaimed in claim 1, wherein the determination is that the visualrepresentation of the base station is present in the captured visualdata, and the switch is from the second mode to the first mode.
 3. Themethod as claimed in claim 1, wherein the determination is that thevisual representation of the base station is not present in the capturedvisual data, and the switch is from the first mode to the second mode.4. The method as claimed in claim 1, wherein the visual representationof at least part of the base station further includes one or morefeatures in surroundings of the base station.
 5. A computer programproduct comprising instructions which, when the program is executed by acomputer, cause the computer to carry out the method of claim
 1. 6. Acomputer-readable data carrier having stored thereon the computerprogram of claim
 5. 7. A network node in a cellular telecommunicationsnetwork, the network node having a transceiver, a processor and a memoryconfigured to cooperate to carry out the method of claim 1.